The present invention generally relates to a system and method for monitoring for structural defects in structures or impacts on structures on the basis of acoustic emission from such defects or impacts.
Stress corrosion and fatigue in structures causes crack growth. This is due to the metal slowly becoming brittle when there is a concentration of stress within a short distance of the crack tip. The crack then advances to a zone boundary in a series of discrete microfracture events, where the microfractures can take place either intergranularly or transgranularly. Tougher undamaged material at the zone boundary stops the crack advancing. The cycle of cracking is then repeated, starting again with a concentration of stress at or near the crack tip.
Under normal operating conditions, damage such as cracking in a structure develops slowly over time. However, if the structure is operating outside its normal range, a large amount of damage may occur within a short time. In addition, damage caused by stress to a structure is not limited to cracking and may also include fretting, pitting and rubbing. It is therefore essential that structures be monitored regularly so that damage may be detected and repaired, or further damage prevented if the damage is not advanced.
Cracking and fracturing is known to cause particular problems in aircraft, pressure vessels and oilrigs, as well as in large structures such as bridges. As cracking occurs, the cracks produce bursts of acoustic energy as wideband ultrasonic emissions in the structure where the cracking is taking place, known as acoustic emissions. The properties of the waveform of the acoustic emissions, such as frequency, amplitude, rise time etc, along with the exact times that bursts of acoustic energy are received at different locations, are dependent on the size of the crack, the location of the crack and how rapidly it propagates through the structure. Therefore cracks can be identified by their acoustic emission signature, which can be detected using acoustic sensors as acoustic emission sensors.
US 2003/0140701, the disclosure of which is hereby incorporated by reference, discloses a method of detecting and monitoring damage in a structure by receiving electrical signals continuously over a period of time as pulses representing a burst of acoustic energy from a plurality of acoustic sensors carried by the structure. The bursts of acoustic energy represent emissions from sites of damage. The burst is processed to obtain a smoothed envelope waveform. Wave-shape information and time information is determined and stored for each burst. If a burst is detected at three or more sensors, the difference in the time of arrival of the bursts at the sensors is determined as Δt values. The Δt values are then used to accumulate the bursts to determine if a threshold for the bursts is exceeded. If so, the burst data is stored to represent structural damage together with non-acoustic parameters.
A limitation in this system is that, when the health of a structure and structural damage is monitored by acoustic emission techniques, errors can occur in the analysis of the data using the system due to the assumption that the speed of sound in structures is uniform in all directions and there is a single mode of acoustic propagation though the structure. However, the speed of sound varies with the thickness and type of material through which the sound is propagating. The speed of propagation of acoustic waves will therefore vary as they propagate through an inhomogeneous structure.
Our previous application, WO2008107668, the disclosure of which is hereby incorporated by reference, addressed this problem by introducing a model of the effect of acoustic paths in a structure in the processing path of the acoustic event data. The model is built by inducing a plurality of types of acoustic emissions at many positions in the structure and by detecting the acoustic emissions using a plurality of acoustic emission sensors that are arranged on the structure. The model takes into account inhomogeneities of the structure, as well as differences in acoustic propagation modes in the structure, so that errors in the location of the damage sites can be reduced.
Referring to FIG. 1, this shows a schematic diagram of an arrangement for locating a site of damage on a typical aircraft structure by detecting acoustic emissions from the site of damage. The aircraft wing has an upper spar cap 101, a lower spar cap 102, a front spar 103, and cross-sectional stiffeners 104. The front spar 103 has reinforcing ribs 107 running vertically at spaced intervals. A fuel aperture 105 is provided on the inside of the front spar 100 and acoustic emission sensors 106 are acoustically coupled to the front spar 103 at several positions.
The illustrated part of the wing 100 is the section of the aircraft wing between the aircraft fuselage and the first engine. The vertical strut 103 supports the upper spar cap 101 and the lower spar cap 102 and the cross-sectional stiffeners 104 add stiffness to the structure of the wing and provide added strength to the front spar 103. The acoustic emission sensors 106 detect acoustic emissions originating from the source of damage on the front spar 103. The acoustic emission sensors are piezoelectric transducers with a resonant frequency in the range of the resonant frequency of the structure under investigation. In aluminium structures, transducers with resonant frequencies of around 200-300 kHz are suitable. The sensors 106 are attached to the structure of the front spar 103 by means of cable ties and self-adhesive bases. In addition, a sealant is used as a joining compound between the base of the sensor and the structure in order to provide a low attenuation acoustic coupling.
Differences in the time of arrival (Δt) of features, such as the leading edges of acoustic emission signals from sources of damage on the structure of the front spar 103 at the sensors 106 or the times of the peak signals from each sensor are used by a triangulation algorithm in analysis software to locate the source of the acoustic emission and therefore the damage. Although the surfaces of modern aircraft structures tend to be substantially homogeneous, discontinuities in the structure result from components in the interior of the structures, for example the vertical struts 103, the cross-sectional stiffeners 104 and the fuel aperture 105. Older aircraft also have surface discontinuities, since their structures consist primarily of riveted and bolted extruded or machined aluminium sections and plates.
For example, the discontinuities and inhomogeneities in the structure of the front spar 100 will cause an acoustic path from point A to point B shown in FIG. 1 to be non-uniform, as the acoustic transmission speed will change as it propagates through the structure. This leads to errors in Δt in the triangulation algorithm, which in turn leads to errors in the location of the site of damage on the aircraft structure. As mentioned above, this problem was addressed by our previous application, WO2008107668.
A schematic diagram of a system 110 for detecting and acquiring acoustic emission data from a structure is shown in FIG. 2. This system is known in the art and a similar system is described in US 2003/0140701. A sensor 111 is coupled to a preamplifier 112, which is connected to a data acquisition unit 113. The data acquisition unit 113 comprises a logarithmic amplifier 114 and a pulse processor unit 115. The data acquisition unit 113 is connected to a computer 116.
Acoustic emissions from sites of damage on the structure are detected by sensors 111, which comprise the acoustic emission sensors 106 placed on an aircraft structure as shown in FIG. 1. The sensors 111 are acoustically coupled to the aircraft structure and can be, for example, a piezoelectric sensor with a resonant frequency in the range from 20 kHz to 2 MHz. Any damage such as cracking on an aircraft structure will emit acoustic waves with a fundamental frequency equal to the resonant frequency of the structures. The resonant frequency of the sensor should therefore be the same as that of the structure being investigated. Typically aluminium aircraft structures have a resonant frequency in the region of 300 kHz, so this is the preferred frequency of sensor to use for detecting acoustic emissions from an aircraft structure. In practice the sensors generally have a bandwidth of a few hundred kHz and sample acoustic data at 15 MHz.
The preamplifier 112 is located in the vicinity of the sensor. There is an array of sensors and preamplifiers having N channels, each channel having one sensor 111 and one preamplifier 112. When calculating the Δt values of acoustic emissions, at least three sensors are required for triangulation. The sensors are acoustically coupled to the structure in spaced apart locations. Each sensor 111 is connected to the data acquisition unit 113 for acquiring and processing acoustic emission data from acoustic emission pulses.
The sensors 111 and preamplifiers 112 are connected to the data acquisition unit 113. The distance between the sensors 111 and the data acquisition unit 113 is installation dependant. In practice, when detection of acoustic emissions takes place from an aircraft structure, the data acquisition unit 113 is located within the avionics bay of the aircraft and is powered from the aircraft's power supply.
There will be background noise from sources such as the aircraft engines that will interfere with acoustic emission signals from the aircraft structure. The data acquisition unit 113 conditions the acoustic emission signals received at the sensor 111 and performs real time filtering and signal processing to isolate acoustic emissions from background noise and produce acoustic emission data that can be used to locate a source of damage on the aircraft structure. The signal received at the sensor takes the form of a wave packet. In each channel the logarithmic amplifier 114 rectifies the signal received from the preamplifier 112. The rectified signal then enters the pulse processor unit 115, which converts the acoustic emission signals received at the sensor 111 to digital signals, filters the digital signals and isolates the digital signals in order to distinguish acoustic emissions received from damage on the structure from background noise.
The digital signals from each channel take the form of pulses, which are analysed by a computer 116 using a triangulation algorithm. Each sensor 111 is generally at a different distance from the site of damage, which means that acoustic emission signals from the damage will reach each sensor at a different time. The location of the damage can be identified by measuring the difference in times of arrival between sensors (Δt) and using acoustic velocity information for each sensor 111. This process is known as triangulation. However, triangulation assumes a homogeneous structure and a uniform speed of sound in all directions in the structure, so there will be errors in the location of the damage as calculated by this method.
As can be seen from the above discussion, prior art systems may have long, and differing, runs of cables between the preamplifiers (local to each sensor) and the data acquisition unit, which can introduce noise in the received signal, and can cause errors to be introduced into the triangulation of the detected acoustic event due to errors in the timing data.